LISA通过自学习划分空间区域，提升交通事故预测精度。
LISA: Learning-Integrated Space Partitioning Framework for Traffic Accident Forecasting on Heterogeneous Spatiotemporal Data
- 边训练模型边自动划分空间，分区由预测准确率引导
- 在真实数据上使基线模型平均提升13.0%的预测精度
- 适合需要处理多尺度异质交通数据的智能系统研究者
交通事故发生预测对智能交通管理和应急响应至关重要。然而，由于环境的空间异质性，该任务极具挑战。现有数据驱动方法多聚焦于有限规模的同质区域（如纽约市单一城市），难以应对不同尺度下的空间异质事故模式。近期方法（如空间集成）依赖预定义空间划分并训练多个模型以提升精度，但需外部知识设定分区，且预设分区未必能降低异质性。为此，我们提出新型学习-集成空间划分框架（LISA），实现分区与模型训练同步进行，分区过程显式由预测准确性引导，而非其他因素。在真实世界数据集上的实验表明，本方法可自引导地捕捉底层异质模式，并使基线网络平均提升13.0%的性能。
原文摘要 · Abstract (English)
Traffic accident forecasting is an important task for intelligent transportation management and emergency response systems. However, this problem is challenging due to the spatial heterogeneity of the environment. Existing data-driven methods mostly focus on studying homogeneous areas with limited size (e.g. a single urban area such as New York City) and fail to handle the heterogeneous accident patterns over space at different scales. Recent advances (e.g. spatial ensemble) utilize pre-defined space partitions and learn multiple models to improve prediction accuracy. However, external knowledge is required to define proper space partitions before training models and pre-defined partitions may not necessarily reduce the heterogeneity. To address this issue, we propose a novel Learning-Integrated Space Partition Framework (LISA) to simultaneously learn partitions while training models, where the partitioning process and learning process are integrated in a way that partitioning is guided explicitly by prediction accuracy rather than other factors. Experiments using real-world datasets, demonstrate that our work can capture underlying heterogeneous patterns in a self-guided way and substantially improve baseline networks by an average of 13.0%.
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